Zero-Trust enforcement for enterprise AI.
DeepInspect authorizes, redacts, or blocks every AI request inline, then writes a cryptographically signed forensic record for regulatory defense.
Four surfaces of the AI governance problem, one control plane.
The gateway sits in the request path with sub-50ms overhead, evaluates policy, and writes an evidence-grade record before the response is released.
Intelligent Security
Identity- and data-aware enforcement. Inline. Fail-closed.
Verifiable Governance
HMAC-signed forensic record for every decision.
Cost & Routing
Tier-based model routing, failover, per-token attribution.
Agent & MCP Governance
Per-tool policy and signed agent traces across every hop.
Who Owns AI Governance Liability in an Enterprise?
AI governance is a responsibility boundary. Regulators and boards want enforcement records, not dashboards.
The boundary falls on the enterprise rather than the model provider. Provider terms of service place contractual responsibility for how a model is used inside a customer environment on the customer. A regulatory inquiry about a specific AI decision reaches the CISO and the compliance officer, and the durable answer is a runtime record of how each AI request was handled together with the policy version in effect at the time.
Enterprises own:
Each obligation maps to an operational record only the enterprise can produce. Regulatory exposure resolves when the enterprise shows the specific decisions the system made and the policy versions in effect. Audit outcomes improve when the auditor reads records that each carry a per-record cryptographic signature verifiable on its own. Breach narratives hold together when every AI interaction in the sequence carries its own integrity proof. Board accountability follows the pattern of financial controls, signed attestations rather than best-effort summaries.
Governance requires reconstruction of who accessed what data and why a decision was allowed.
The risk lives in ungoverned AI usage inside your enterprise.
Point your AI client at the gateway. Nothing else changes.
Enforcement activates gradually from observe mode to enforce mode, per application and per data class. Application code stays intact.
Product
Inline enforcement between callers and models.
Identity-aware policy, deterministic evaluation, fail-closed by default. Sub-50ms overhead in the request path.
Forensics
Signed record for every AI decision.
Reconstruct any interaction. Verify integrity per record.
Architecture
Deterministic policy engine, default-deny posture.
Deploys inline. Payload-agnostic. Provider-neutral.
< 50ms
Gateway overhead
100%
Decisions signed
6+
Providers supported
HMAC
Per-record integrity
Recent thinking.
All posts →September 6, 2026
HITRUST AI Compliance Checklist: 9 Tests
A HITRUST AI compliance checklist for protected data flows: 9 tests covering assessment scope, model inventory, identity propagation, data classification, destination rules, response handling, audit evidence, vendor evidence, and recurring tests. Each test produces proof tied to actual AI request traffic, the kind an assessor can sample against real events rather than a written policy alone.
September 6, 2026
5 Datadog LLM Observability Alternatives, By Category
Datadog LLM Observability logs prompts and responses after the model call, which is why it cannot block or redact one before it happens. This breakdown compares the five categories a CISO or AI platform lead should separate before shortlisting a tool: LLM eval platforms, APM vendors with AI tracing bolted on, pre-deployment testing tools, AI-aware data security tools, and inline enforcement gateways that decide before the request reaches the model.
September 6, 2026
OpenAI Cannot Rule Out Critical Cyber Capability In Astra
OpenAI cannot rule out Critical cyber capability in Astra, an upcoming model, under its own Preparedness Framework. Four of the five safeguards it names are the lab's internal security programme: isolated testing, weight encryption, sandboxing, and chain-of-thought monitoring. One item survives deployment unchanged, restricted network and tool access, and it becomes a per-request authorization decision on your side of the boundary the moment the model ships. This piece names what produces the audit record when someone asks which instruction produced which outbound call.